(College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia)(2) Mona Alshenaifi
(College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia)(3) Abeer Aljaloud
(College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia)(4) Tamadhur Albednah
(College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia)(5) Reham Alghanim
(College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia)(6) Alanoud Alqifari
(College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia)(7) Amal Alqahtani
(College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia)*corresponding author
AbstractBreast cancer recurrence is among the most noteworthy fears faced by women. Nevertheless, with modern innovations in data mining technology, early recurrence prediction can help relieve these fears. Although medical information is typically complicated, and simplifying searches to the most relevant input is challenging, new sophisticated data mining techniques promise accurate predictions from high-dimensional data. In this study, the performances of three established data mining algorithms: Naïve Bayes (NB), k-nearest neighbor (KNN), and fast decision tree (REPTree), adopting the feature extraction algorithm, principal component analysis (PCA), for predicting breast cancer recurrence were contrasted. The comparison was conducted between models built in the absence and presence of PCA. The results showed that KNN produced better prediction without PCA (F-measure = 72.1%), whereas the other two techniques: NB and REPTree, improved when used with PCA (F-measure = 76.1% and 72.8%, respectively). This study can benefit the healthcare industry in assisting physicians in predicting breast cancer recurrence precisely.
KeywordsBreast cancer recurrence; Data Mining; Feature Extraction; Machine Learning; Principal Component Analysis
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DOIhttps://doi.org/10.26555/ijain.v6i3.462 |
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